Simulation, Optimization, and Machine Learning for Finance, second edition

(Hardback)

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A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making. A comprehensive guide to simulation, optimization, and machine learning for finance, covering theoretical foundations, practical applications, and data-driven decision-making. Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible. Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation. Provides a structured introduction to probability, inferential statistics, and data science Explores cutting-edge techniques in simulation modeling, optimization, and machine learning Demonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative tools Covers factor models and stochastic processes in asset pricing Integrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-making Is suitable for practitioners, students, and self-learners

Author Bio

Dessislava A. Pachamanova is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology. She is coauthor of Robust Portfolio Optimization and Management and Portfolio Construction and Analytics. Frank J. Fabozzi is Professor of Practice in Finance at Johns Hopkins' Carey Business School, author of Introduction to Fixed-Income Analysis and Portfolio Management; Capital Markets, sixth edition; and Entrepreneurial Finance and Accounting for High-Tech Companies, and coauthor of Bond Markets, Analysis, and Strategies, tenth edition; Foundations of Global Financial Markets and Institutions; and The Economics of FinTech, all published by the MIT Press. Francesco A. Fabozzi is Research Director at Yale School of Management's International Center for Finance. He serves as the Managing Editor of The Journal of Financial Data Science and the Director of Data Science at the CFA Institute Research Foundation and is the coauthor of six books in asset management and corporate finance.